Overview

Dataset statistics

Number of variables8
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory66.5 KiB
Average record size in memory68.1 B

Variable types

Categorical3
Numeric3
Boolean1
DateTime1

Dataset

DescriptionMBS 배당수익계산가중편균정보에 대한 데이터로, 유동화계획코드, 가중편균금리(금융기관), 일치여부, 등록사번 등의 항목을 제공합니다.
Author한국주택금융공사
URLhttps://www.data.go.kr/data/15073183/fileData.do

Alerts

LIQD_PLAN_CD is highly overall correlated with HF_WA_RAT and 2 other fieldsHigh correlation
LOAN_ORG_CD is highly overall correlated with HF_WA_RAT and 2 other fieldsHigh correlation
BASIS_DY is highly overall correlated with REG_ENOHigh correlation
HF_WA_RAT is highly overall correlated with FI_WA_RAT and 2 other fieldsHigh correlation
FI_WA_RAT is highly overall correlated with HF_WA_RAT and 2 other fieldsHigh correlation
REG_ENO is highly overall correlated with BASIS_DYHigh correlation
SAME_YN is highly imbalanced (98.9%)Imbalance

Reproduction

Analysis started2023-12-12 05:18:53.830120
Analysis finished2023-12-12 05:18:55.603785
Duration1.77 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

LOAN_ORG_CD
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
I004
437 
I001
282 
I003
107 
F001
87 
F002
58 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowI003
2nd rowI003
3rd rowF001
4th rowF001
5th rowI003

Common Values

ValueCountFrequency (%)
I004 437
43.7%
I001 282
28.2%
I003 107
 
10.7%
F001 87
 
8.7%
F002 58
 
5.8%
F003 29
 
2.9%

Length

2023-12-12T14:18:55.658664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:18:55.759060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i004 437
43.7%
i001 282
28.2%
i003 107
 
10.7%
f001 87
 
8.7%
f002 58
 
5.8%
f003 29
 
2.9%

LIQD_PLAN_CD
Categorical

HIGH CORRELATION 

Distinct36
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
KHFCMB2013S-24
 
29
KHFCMB2016S-02
 
29
KHFCMB2016S-18
 
29
KHFCMB2012S-36
 
29
KHFCMB2012S-12
 
29
Other values (31)
855 

Length

Max length14
Median length14
Mean length14
Min length14

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKHFCMB2019S-01
2nd rowKHFCMB2013S-33
3rd rowKHFCMB2012S-36
4th rowKHFCMB2012S-12
5th rowKHFCMB2017S-24

Common Values

ValueCountFrequency (%)
KHFCMB2013S-24 29
 
2.9%
KHFCMB2016S-02 29
 
2.9%
KHFCMB2016S-18 29
 
2.9%
KHFCMB2012S-36 29
 
2.9%
KHFCMB2012S-12 29
 
2.9%
KHFCMB2017S-24 29
 
2.9%
KHFCMB2013S-11 29
 
2.9%
KHFCMB2014S-04 29
 
2.9%
KHFCMB2013S-13 29
 
2.9%
KHFCMB2012S-27 29
 
2.9%
Other values (26) 710
71.0%

Length

2023-12-12T14:18:55.867839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
khfcmb2013s-24 29
 
2.9%
khfcmb2013s-12 29
 
2.9%
khfcmb2013s-38 29
 
2.9%
khfcmb2017s-06 29
 
2.9%
khfcmb2017s-21 29
 
2.9%
khfcmb2018s-07 29
 
2.9%
khfcmb2015s-19 29
 
2.9%
khfcmb2015s-08 29
 
2.9%
khfcmb2016s-02 29
 
2.9%
khfcmb2014s-22 29
 
2.9%
Other values (26) 710
71.0%

BASIS_DY
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20191328
Minimum20180501
Maximum20200901
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-12T14:18:55.980423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20180501
5-th percentile20180601
Q120181203
median20190701
Q320200301
95-th percentile20200803
Maximum20200901
Range20400
Interquartile range (IQR)19098

Descriptive statistics

Standard deviation7465.8597
Coefficient of variation (CV)0.00036975575
Kurtosis-1.2600682
Mean20191328
Median Absolute Deviation (MAD)9500
Skewness-0.10701343
Sum2.0191328 × 1010
Variance55739061
MonotonicityNot monotonic
2023-12-12T14:18:56.171425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
20200901 36
 
3.6%
20200701 36
 
3.6%
20200601 36
 
3.6%
20200401 36
 
3.6%
20191001 35
 
3.5%
20190201 35
 
3.5%
20190401 35
 
3.5%
20190701 35
 
3.5%
20190801 35
 
3.5%
20191101 35
 
3.5%
Other values (34) 646
64.6%
ValueCountFrequency (%)
20180501 10
 
1.0%
20180502 15
1.5%
20180601 33
3.3%
20180701 18
1.8%
20180702 15
1.5%
20180801 33
3.3%
20180901 18
1.8%
20180903 15
1.5%
20181001 33
3.3%
20181101 33
3.3%
ValueCountFrequency (%)
20200901 36
3.6%
20200803 18
1.8%
20200801 18
1.8%
20200701 36
3.6%
20200601 36
3.6%
20200504 18
1.8%
20200501 18
1.8%
20200401 36
3.6%
20200302 18
1.8%
20200301 18
1.8%

HF_WA_RAT
Real number (ℝ)

HIGH CORRELATION 

Distinct164
Distinct (%)16.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.52423
Minimum2.36
Maximum4.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-12T14:18:56.326411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.36
5-th percentile2.7895
Q13.33
median3.51
Q33.83
95-th percentile4.2205
Maximum4.65
Range2.29
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.43068306
Coefficient of variation (CV)0.12220629
Kurtosis0.69777386
Mean3.52423
Median Absolute Deviation (MAD)0.27
Skewness-0.23142287
Sum3524.23
Variance0.18548789
MonotonicityNot monotonic
2023-12-12T14:18:56.456328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.52 35
 
3.5%
3.49 32
 
3.2%
3.53 29
 
2.9%
3.4 28
 
2.8%
3.56 27
 
2.7%
3.36 26
 
2.6%
3.57 26
 
2.6%
3.51 22
 
2.2%
3.87 21
 
2.1%
3.48 20
 
2.0%
Other values (154) 734
73.4%
ValueCountFrequency (%)
2.36 5
 
0.5%
2.37 17
1.7%
2.38 5
 
0.5%
2.39 2
 
0.2%
2.42 2
 
0.2%
2.51 1
 
0.1%
2.55 3
 
0.3%
2.56 1
 
0.1%
2.67 2
 
0.2%
2.68 3
 
0.3%
ValueCountFrequency (%)
4.65 5
0.5%
4.64 3
0.3%
4.63 3
0.3%
4.62 3
0.3%
4.61 1
 
0.1%
4.59 1
 
0.1%
4.56 1
 
0.1%
4.53 1
 
0.1%
4.48 1
 
0.1%
4.44 2
 
0.2%

FI_WA_RAT
Real number (ℝ)

HIGH CORRELATION 

Distinct164
Distinct (%)16.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.52424
Minimum2.36
Maximum4.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-12T14:18:56.606004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.36
5-th percentile2.7895
Q13.33
median3.515
Q33.83
95-th percentile4.2205
Maximum4.65
Range2.29
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.43068284
Coefficient of variation (CV)0.12220588
Kurtosis0.69780283
Mean3.52424
Median Absolute Deviation (MAD)0.265
Skewness-0.23149298
Sum3524.24
Variance0.18548771
MonotonicityNot monotonic
2023-12-12T14:18:56.756612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.52 36
 
3.6%
3.49 32
 
3.2%
3.53 29
 
2.9%
3.4 28
 
2.8%
3.56 27
 
2.7%
3.36 26
 
2.6%
3.57 26
 
2.6%
3.51 21
 
2.1%
3.87 21
 
2.1%
3.48 20
 
2.0%
Other values (154) 734
73.4%
ValueCountFrequency (%)
2.36 5
 
0.5%
2.37 17
1.7%
2.38 5
 
0.5%
2.39 2
 
0.2%
2.42 2
 
0.2%
2.51 1
 
0.1%
2.55 3
 
0.3%
2.56 1
 
0.1%
2.67 2
 
0.2%
2.68 3
 
0.3%
ValueCountFrequency (%)
4.65 5
0.5%
4.64 3
0.3%
4.63 3
0.3%
4.62 3
0.3%
4.61 1
 
0.1%
4.59 1
 
0.1%
4.56 1
 
0.1%
4.53 1
 
0.1%
4.48 1
 
0.1%
4.44 2
 
0.2%

SAME_YN
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
True
999 
False
 
1
ValueCountFrequency (%)
True 999
99.9%
False 1
 
0.1%
2023-12-12T14:18:56.854364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

REG_ENO
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1438
308 
1769
265 
1841
245 
1628
124 
1460
58 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1769
2nd row1769
3rd row1769
4th row1769
5th row1769

Common Values

ValueCountFrequency (%)
1438 308
30.8%
1769 265
26.5%
1841 245
24.5%
1628 124
12.4%
1460 58
 
5.8%

Length

2023-12-12T14:18:56.940335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:18:57.036333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1438 308
30.8%
1769 265
26.5%
1841 245
24.5%
1628 124
12.4%
1460 58
 
5.8%

REG_DT
Date

Distinct670
Distinct (%)67.0%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Minimum2018-05-31 10:32:35
Maximum2020-10-13 20:19:27
2023-12-12T14:18:57.155273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:57.361116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-12-12T14:18:55.068088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:54.276536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:54.652986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:55.211939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:54.407737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:54.810159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:55.310988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:54.523367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:54.942785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:18:57.739275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
LOAN_ORG_CDLIQD_PLAN_CDBASIS_DYHF_WA_RATFI_WA_RATSAME_YNREG_ENO
LOAN_ORG_CD1.0001.0000.0000.7580.7580.0000.000
LIQD_PLAN_CD1.0001.0000.0000.9370.9370.0000.000
BASIS_DY0.0000.0001.0000.4030.4030.0120.786
HF_WA_RAT0.7580.9370.4031.0001.0000.0000.399
FI_WA_RAT0.7580.9370.4031.0001.0000.0000.399
SAME_YN0.0000.0000.0120.0000.0001.0000.000
REG_ENO0.0000.0000.7860.3990.3990.0001.000
2023-12-12T14:18:57.863282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
LIQD_PLAN_CDLOAN_ORG_CDSAME_YNREG_ENO
LIQD_PLAN_CD1.0000.9850.0000.000
LOAN_ORG_CD0.9851.0000.0000.000
SAME_YN0.0000.0001.0000.000
REG_ENO0.0000.0000.0001.000
2023-12-12T14:18:57.977450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
BASIS_DYHF_WA_RATFI_WA_RATLOAN_ORG_CDLIQD_PLAN_CDSAME_YNREG_ENO
BASIS_DY1.000-0.208-0.2090.0000.0000.0100.743
HF_WA_RAT-0.2081.0001.0000.5300.6820.0000.176
FI_WA_RAT-0.2091.0001.0000.5300.6820.0000.176
LOAN_ORG_CD0.0000.5300.5301.0000.9850.0000.000
LIQD_PLAN_CD0.0000.6820.6820.9851.0000.0000.000
SAME_YN0.0100.0000.0000.0000.0001.0000.000
REG_ENO0.7430.1760.1760.0000.0000.0001.000

Missing values

2023-12-12T14:18:55.435657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:18:55.557811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

LOAN_ORG_CDLIQD_PLAN_CDBASIS_DYHF_WA_RATFI_WA_RATSAME_YNREG_ENOREG_DT
0I003KHFCMB2019S-01202009013.863.86Y17692020/10/13 20:19:27
1I003KHFCMB2013S-33202009013.513.51Y17692020/10/13 20:19:27
2F001KHFCMB2012S-36202009012.752.75Y17692020/10/13 20:09:11
3F001KHFCMB2012S-12202009013.093.09Y17692020/10/13 20:09:11
4I003KHFCMB2017S-24202009013.783.78Y17692020/10/13 20:19:27
5I003KHFCMB2013S-11202009013.093.09Y17692020/10/13 20:19:27
6F001KHFCMB2014S-04202009012.992.99Y17692020/10/13 20:09:11
7F002KHFCMB2013S-12202009012.392.39Y17692020/10/13 16:01:40
8F002KHFCMB2012S-27202009012.422.42Y17692020/10/13 16:01:40
9I001KHFCMB2013S-10202009013.243.24Y17692020/10/13 15:44:24
LOAN_ORG_CDLIQD_PLAN_CDBASIS_DYHF_WA_RATFI_WA_RATSAME_YNREG_ENOREG_DT
990I004KHFCMB2013S-13201805013.63.6Y14602018/05/31 10:39:18
991I004KHFCMB2012S-38201805014.254.25Y14602018/05/31 10:39:18
992I004KHFCMB2012S-26201805014.624.62Y14602018/05/31 10:39:17
993F002KHFCMB2013S-12201805012.362.36Y14602018/05/31 10:38:20
994F002KHFCMB2012S-27201805012.762.76Y14602018/05/31 10:38:20
995F001KHFCMB2014S-04201805023.543.54Y14602018/05/31 10:33:52
996F001KHFCMB2012S-36201805013.493.49Y14602018/05/31 10:33:44
997F001KHFCMB2012S-12201805013.683.68Y14602018/05/31 10:33:44
998I001KHFCMB2013S-10201805013.283.28Y14602018/05/31 10:32:36
999I001KHFCMB2012S-37201805013.33.3Y14602018/05/31 10:32:35